In contrast to the common belief introduced in [1]}We also reproduce these experiments in Appendix . that a model first learns the simple and general patterns (easy examples) before fitting the noise (difficult examples), we provide a refined insight below: easy and difficult examples are learnt by the model simultaneously, with easy examples learnt faster.
As indicated in Section REF , in the presence of label corruption, data with random labels are of the highest difficulty.
